29 lines
832 B
Python
29 lines
832 B
Python
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import sys
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import pandas as pd
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import numpy as np
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import tensorflow as tf
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import os.path
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from tensorflow import keras
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from tensorflow.keras import layers
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from tensorflow.keras.layers.experimental import preprocessing
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# Wczytanie danych
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test_data = pd.read_csv("./MoviesOnStreamingPlatforms_updated.test")
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columns_to_use = ['Year', 'Runtime', 'Netflix']
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test_X = tf.convert_to_tensor(test_data[columns_to_use])
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test_Y = tf.convert_to_tensor(test_data[["IMDb"]])
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model = tf.keras.models.load_model('linear_regression.h5')
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scores = model.evaluate(x=test_X,
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y=test_Y)
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with open('single_metrics.txt', 'w') as file:
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for idx, score in enumerate(scores):
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if idx == 0:
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file.write("Recall: " + str(score) + "\n")
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if idx == 1:
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file.write("RMSE: " + str(score) + "\n")
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